# coding=utf-8 # Copyright 2023 The Pop2Piano Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Pop2Piano model.""" import copy import math from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from transformers.generation import GenerationConfig from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_flex_attn_available, is_torch_fx_proxy, is_torchdynamo_compiling, logging, replace_return_docstrings, ) from .configuration_pop2piano import Pop2PianoConfig if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from ...integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) _load_pop2piano_layer_norm = True try: from apex.normalization import FusedRMSNorm _load_pop2piano_layer_norm = False logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of Pop2PianoLayerNorm") except ImportError: # using the normal Pop2PianoLayerNorm pass except Exception: logger.warning("Discovered apex but it failed to load, falling back to Pop2PianoLayerNorm") pass _CONFIG_FOR_DOC = "Pop2PianoConfig" _CHECKPOINT_FOR_DOC = "sweetcocoa/pop2piano" POP2PIANO_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Pop2Piano is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [Pop2Piano Training](./Pop2Piano#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Pop2Piano uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Does the same task as `inputs_embeds`. If `inputs_embeds` is not present but `input_features` is present then `input_features` will be considered as `inputs_embeds`. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache in the correct position and to infer the complete sequence length. """ # Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pop2Piano class Pop2PianoLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the Pop2Piano style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # Pop2Piano uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states if not _load_pop2piano_layer_norm: Pop2PianoLayerNorm = FusedRMSNorm # noqa ALL_LAYERNORM_LAYERS.append(Pop2PianoLayerNorm) # Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->Pop2Piano,t5->pop2piano class Pop2PianoDenseActDense(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pop2Piano class Pop2PianoDenseGatedActDense(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. # See https://github.com/huggingface/transformers/issues/20287 # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->Pop2Piano class Pop2PianoLayerFF(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() if config.is_gated_act: self.DenseReluDense = Pop2PianoDenseGatedActDense(config) else: self.DenseReluDense = Pop2PianoDenseActDense(config) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5Attention with T5->Pop2Piano,t5->pop2piano class Pop2PianoAttention(nn.Module): def __init__( self, config: Pop2PianoConfig, has_relative_attention_bias=False, layer_idx: Optional[int] = None, ): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim self.layer_idx = layer_idx if layer_idx is None and self.is_decoder: logger.warning_once( f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) self.pruned_heads = set() self.gradient_checkpointing = False def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.key_value_proj_dim * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device=None, cache_position=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device if cache_position is None: context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] else: context_position = cache_position[:, None].to(device) memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, cache_position=None, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder) batch_size, seq_length = hidden_states.shape[:2] # if key_value_states are provided this layer is used as a cross-attention layer for the decoder is_cross_attention = key_value_states is not None query_states = self.q(hidden_states) query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) if past_key_value is not None: is_updated = past_key_value.is_updated.get(self.layer_idx) if is_cross_attention: # after the first generated id, we can subsequently re-use all key/value_states from cache curr_past_key_value = past_key_value.cross_attention_cache else: curr_past_key_value = past_key_value.self_attention_cache current_states = key_value_states if is_cross_attention else hidden_states if is_cross_attention and past_key_value is not None and is_updated: # reuse k,v, cross_attentions key_states = curr_past_key_value.key_cache[self.layer_idx] value_states = curr_past_key_value.value_cache[self.layer_idx] else: key_states = self.k(current_states) value_states = self.v(current_states) key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) if past_key_value is not None: # save all key/value_states to cache to be re-used for fast auto-regressive generation cache_position = cache_position if not is_cross_attention else None key_states, value_states = curr_past_key_value.update( key_states, value_states, self.layer_idx, {"cache_position": cache_position} ) # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls if is_cross_attention: past_key_value.is_updated[self.layer_idx] = True # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 scores = torch.matmul(query_states, key_states.transpose(3, 2)) if position_bias is None: key_length = key_states.shape[-2] # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past) real_seq_length = query_length if query_length is not None else cache_position[-1] + 1 if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias( real_seq_length, key_length, device=scores.device, cache_position=cache_position ) position_bias = position_bias[:, :, -seq_length:, :] if mask is not None: causal_mask = mask[:, :, :, : key_states.shape[-2]] position_bias = position_bias + causal_mask if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(batch_size, -1, self.inner_dim) attn_output = self.o(attn_output) outputs = (attn_output, past_key_value, position_bias) if output_attentions: outputs = outputs + (attn_weights,) return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->Pop2Piano,t5->pop2piano class Pop2PianoLayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None): super().__init__() self.SelfAttention = Pop2PianoAttention( config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx ) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, cache_position=None, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->Pop2Piano,t5->pop2piano class Pop2PianoLayerCrossAttention(nn.Module): def __init__(self, config, layer_idx: Optional[int] = None): super().__init__() self.EncDecAttention = Pop2PianoAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, cache_position=None, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, cache_position=cache_position, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs # Copied from transformers.models.t5.modeling_t5.T5Block with T5->Pop2Piano,t5->pop2piano class Pop2PianoBlock(nn.Module): def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append( Pop2PianoLayerSelfAttention( config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx ) ) if self.is_decoder: self.layer.append(Pop2PianoLayerCrossAttention(config, layer_idx=layer_idx)) self.layer.append(Pop2PianoLayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, cache_position=None, ): self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states, past_key_value = self_attention_outputs[:2] attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, query_length=cache_position[-1] + 1, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, past_key_value = cross_attention_outputs[:2] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if use_cache: outputs = outputs + (past_key_value,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, past_key_value, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class Pop2PianoPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Pop2PianoConfig base_model_prefix = "transformer" is_parallelizable = False supports_gradient_checkpointing = True _supports_cache_class = True _supports_static_cache = False _no_split_modules = ["Pop2PianoBlock"] _keep_in_fp32_modules = ["wo"] def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, Pop2PianoLayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance(module, Pop2PianoConcatEmbeddingToMel): module.embedding.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, Pop2PianoForConditionalGeneration): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, Pop2PianoDenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, Pop2PianoDenseGatedActDense): module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_() module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, Pop2PianoAttention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id if decoder_start_token_id is None: raise ValueError( "self.model.config.decoder_start_token_id has to be defined. In Pop2Piano it is usually set to the pad_token_id." ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class Pop2PianoStack(Pop2PianoPreTrainedModel): # Copied from transformers.models.t5.modeling_t5.T5Stack.__init__ with T5->Pop2Piano,t5->pop2piano def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [ Pop2PianoBlock(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) for i in range(config.num_layers) ] ) self.final_layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False # Copied from transformers.models.t5.modeling_t5.T5Stack.get_input_embeddings def get_input_embeddings(self): return self.embed_tokens # Copied from transformers.models.t5.modeling_t5.T5Stack.set_input_embeddings def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None, ): use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: if self.embed_tokens is None: raise ValueError("You have to initialize the model with valid token embeddings") inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape if use_cache is True: if not self.is_decoder: raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") # initialize past_key_values return_legacy_cache = False return_self_attention_cache = False if self.is_decoder and (use_cache or past_key_values is not None): if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache): return_self_attention_cache = True past_key_values = EncoderDecoderCache(past_key_values, DynamicCache()) elif not isinstance(past_key_values, EncoderDecoderCache): return_legacy_cache = True logger.warning_once( "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. " "You should pass an instance of `EncoderDecoderCache` instead, e.g. " "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`." ) past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values) elif past_key_values is None: past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache()) elif not self.is_decoder: # do not pass cache object down the line for encoder stack # it messes indexing later in decoder-stack because cache object is modified in-place past_key_values = None past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 if cache_position is None: cache_position = torch.arange( past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device ) if attention_mask is None and not is_torchdynamo_compiling(): # required mask seq length can be calculated via length of past cache mask_seq_length = past_key_values_length + seq_length attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.config.is_decoder: causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values.self_attention_cache if past_key_values is not None else None, output_attentions, ) else: causal_mask = attention_mask[:, None, None, :] causal_mask = causal_mask.to(dtype=inputs_embeds.dtype) causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, layer_module in enumerate(self.block): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.forward, hidden_states, causal_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing use_cache, output_attentions, cache_position, ) else: layer_outputs = layer_module( hidden_states, attention_mask=causal_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_values, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, next_decoder_cache = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_self_attention_cache: next_cache = past_key_values.self_attention_cache if return_legacy_cache: next_cache = past_key_values.to_legacy_cache() if not return_dict: return tuple( v for v in [ hidden_states, next_cache, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) if isinstance(attention_mask, BlockMask): return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class Pop2PianoConcatEmbeddingToMel(nn.Module): """Embedding Matrix for `composer` tokens.""" def __init__(self, config): super().__init__() self.embedding = nn.Embedding(num_embeddings=config.composer_vocab_size, embedding_dim=config.d_model) def forward(self, feature, index_value, embedding_offset): index_shifted = index_value - embedding_offset composer_embedding = self.embedding(index_shifted).unsqueeze(1) inputs_embeds = torch.cat([composer_embedding, feature], dim=1) return inputs_embeds Pop2Piano_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Pop2PianoConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings("""Pop2Piano Model with a `language modeling` head on top.""", Pop2Piano_START_DOCSTRING) class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel, GenerationMixin): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: Pop2PianoConfig): super().__init__(config) self.config = config self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) self.mel_conditioner = Pop2PianoConcatEmbeddingToMel(config) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = Pop2PianoStack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = Pop2PianoStack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_output_embeddings(self): return self.lm_head def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_mel_conditioner_outputs( self, input_features: torch.FloatTensor, composer: str, generation_config: GenerationConfig, attention_mask: Optional[torch.FloatTensor] = None, ): """ This method is used to concatenate mel conditioner tokens at the front of the input_features in order to control the type of MIDI token generated by the model. Args: input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): input features extracted from the feature extractor. composer (`str`): composer token which determines the type of MIDI tokens to be generated. generation_config (`~generation.GenerationConfig`): The generation is used to get the composer-feature_token pair. attention_mask (``, *optional*): For batched generation `input_features` are padded to have the same shape across all examples. `attention_mask` helps to determine which areas were padded and which were not. - 1 for tokens that are **not padded**, - 0 for tokens that are **padded**. """ composer_to_feature_token = generation_config.composer_to_feature_token if composer not in composer_to_feature_token.keys(): raise ValueError( f"Please choose a composer from {list(composer_to_feature_token.keys())}. Composer received - {composer}" ) composer_value = composer_to_feature_token[composer] composer_value = torch.tensor(composer_value, device=self.device) composer_value = composer_value.repeat(input_features.shape[0]) embedding_offset = min(composer_to_feature_token.values()) input_features = self.mel_conditioner( feature=input_features, index_value=composer_value, embedding_offset=embedding_offset, ) if attention_mask is not None: input_features[~attention_mask[:, 0].bool()] = 0.0 # since self.mel_conditioner adds a new array at the front of inputs_embeds we need to do the same for attention_mask to keep the shapes same attention_mask = torch.concatenate([attention_mask[:, 0].view(-1, 1), attention_mask], axis=1) return input_features, attention_mask return input_features, None @add_start_docstrings_to_model_forward(POP2PIANO_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, input_features: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: """ use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None and input_features is not None: raise ValueError("Both `inputs_embeds` and `input_features` received! Please provide only one of them") elif input_features is not None and inputs_embeds is None: inputs_embeds = input_features # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) sequence_output = decoder_outputs[0] if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @torch.no_grad() def generate( self, input_features, attention_mask=None, composer="composer1", generation_config=None, **kwargs, ): """ Generates token ids for midi outputs. Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model's default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. For an overview of generation strategies and code examples, check out the [following guide](./generation_strategies). Parameters: input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): This is the featurized version of audio generated by `Pop2PianoFeatureExtractor`. attention_mask: For batched generation `input_features` are padded to have the same shape across all examples. `attention_mask` helps to determine which areas were padded and which were not. - 1 for tokens that are **not padded**, - 0 for tokens that are **padded**. composer (`str`, *optional*, defaults to `"composer1"`): This value is passed to `Pop2PianoConcatEmbeddingToMel` to generate different embeddings for each `"composer"`. Please make sure that the composet value is present in `composer_to_feature_token` in `generation_config`. For an example please see https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json . generation_config (`~generation.GenerationConfig`, *optional*): The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which had the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s default values, whose documentation should be checked to parameterize generation. kwargs: Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. Return: [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. Since Pop2Piano is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible [`~utils.ModelOutput`] types are: - [`~generation.GenerateEncoderDecoderOutput`], - [`~generation.GenerateBeamEncoderDecoderOutput`] """ if generation_config is None: generation_config = self.generation_config generation_config.update(**kwargs) # check for composer_to_feature_token if not hasattr(generation_config, "composer_to_feature_token"): raise ValueError( "`composer_to_feature_token` was not found! Please refer to " "https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json" "and parse a dict like that." ) if len(generation_config.composer_to_feature_token) != self.config.composer_vocab_size: raise ValueError( "config.composer_vocab_size must be same as the number of keys in " f"generation_config.composer_to_feature_token! " f"Found {self.config.composer_vocab_size} vs {len(generation_config.composer_to_feature_token)}." ) # to control the variation of generated MIDI tokens we concatenate mel-conditioner tokens(which depends on composer_token) # at the front of input_features. input_features, attention_mask = self.get_mel_conditioner_outputs( input_features=input_features, attention_mask=attention_mask, composer=composer, generation_config=generation_config, ) return super().generate( inputs=None, inputs_embeds=input_features, attention_mask=attention_mask, generation_config=generation_config, **kwargs, ) def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past_key_values, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past_key_values is None: logger.warning("You might want to consider setting `use_cache=True` to speed up decoding") return past_key_values reordered_decoder_past = () for layer_past_states in past_key_values: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError( f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched" ) if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError( f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched" ) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past __all__ = ["Pop2PianoForConditionalGeneration", "Pop2PianoPreTrainedModel"]